Overview

Dataset statistics

Number of variables23
Number of observations3676
Missing cells5660
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory639.8 B

Variable types

Categorical10
Text3
Numeric10

Alerts

store room is highly imbalanced (55.7%)Imbalance
super_built_up_area has 1801 (49.0%) missing valuesMissing
built_up_area has 1986 (54.0%) missing valuesMissing
carpet_area has 1805 (49.1%) missing valuesMissing
area is highly skewed (γ1 = 31.62359394)Skewed
carpet_area is highly skewed (γ1 = 24.32675538)Skewed
floorNum has 129 (3.5%) zerosZeros
luxury_score has 462 (12.6%) zerosZeros

Reproduction

Analysis started2024-03-25 13:44:48.321236
Analysis finished2024-03-25 13:44:56.133226
Duration7.81 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

property_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.5 KiB
flat
2817 
house
859 

Length

Max length5
Median length4
Mean length4.2336779
Min length4

Characters and Unicode

Total characters15563
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowhouse
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2817
76.6%
house 859
 
23.4%

Length

2024-03-25T15:44:56.185901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T15:44:56.254519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
flat 2817
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2817
18.1%
l 2817
18.1%
a 2817
18.1%
t 2817
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 2817
18.1%
l 2817
18.1%
a 2817
18.1%
t 2817
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 2817
18.1%
l 2817
18.1%
a 2817
18.1%
t 2817
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 2817
18.1%
l 2817
18.1%
a 2817
18.1%
t 2817
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%
Distinct675
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size293.8 KiB
2024-03-25T15:44:56.570578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.86449
Min length1

Characters and Unicode

Total characters61977
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique307 ?
Unique (%)8.4%

Sample

1st rowss the leaf
2nd rowtulip purple
3rd rowumang monsoon breeze
4th rowindependent
5th rowtulip violet
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
m3m 152
 
1.6%
global 152
 
1.6%
signature 149
 
1.5%
heights 134
 
1.4%
Other values (783) 7493
77.5%
2024-03-25T15:44:56.994646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6707
 
10.8%
5998
 
9.7%
a 5858
 
9.5%
r 4167
 
6.7%
n 4161
 
6.7%
i 3829
 
6.2%
t 3719
 
6.0%
s 3470
 
5.6%
l 2940
 
4.7%
o 2753
 
4.4%
Other values (31) 18375
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61977
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6707
 
10.8%
5998
 
9.7%
a 5858
 
9.5%
r 4167
 
6.7%
n 4161
 
6.7%
i 3829
 
6.2%
t 3719
 
6.0%
s 3470
 
5.6%
l 2940
 
4.7%
o 2753
 
4.4%
Other values (31) 18375
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61977
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6707
 
10.8%
5998
 
9.7%
a 5858
 
9.5%
r 4167
 
6.7%
n 4161
 
6.7%
i 3829
 
6.2%
t 3719
 
6.0%
s 3470
 
5.6%
l 2940
 
4.7%
o 2753
 
4.4%
Other values (31) 18375
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61977
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6707
 
10.8%
5998
 
9.7%
a 5858
 
9.5%
r 4167
 
6.7%
n 4161
 
6.7%
i 3829
 
6.2%
t 3719
 
6.0%
s 3470
 
5.6%
l 2940
 
4.7%
o 2753
 
4.4%
Other values (31) 18375
29.6%

sector
Text

Distinct114
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size266.8 KiB
2024-03-25T15:44:57.263403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3180087
Min length3

Characters and Unicode

Total characters34253
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsector 85
2nd rowsector 69
3rd rowsector 78
4th rowsector 7
5th rowsector 69
ValueCountFrequency (%)
sector 3448
46.7%
road 177
 
2.4%
sohna 165
 
2.2%
85 108
 
1.5%
102 106
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
65 87
 
1.2%
81 86
 
1.2%
Other values (106) 2922
39.6%
2024-03-25T15:44:57.753144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3801
11.1%
3705
10.8%
s 3692
10.8%
r 3692
10.8%
e 3547
10.4%
c 3499
10.2%
t 3459
10.1%
1 1071
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6207
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34253
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3801
11.1%
3705
10.8%
s 3692
10.8%
r 3692
10.8%
e 3547
10.4%
c 3499
10.2%
t 3459
10.1%
1 1071
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6207
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34253
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3801
11.1%
3705
10.8%
s 3692
10.8%
r 3692
10.8%
e 3547
10.4%
c 3499
10.2%
t 3459
10.1%
1 1071
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6207
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34253
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3801
11.1%
3705
10.8%
s 3692
10.8%
r 3692
10.8%
e 3547
10.4%
c 3499
10.2%
t 3459
10.1%
1 1071
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6207
18.1%

price
Real number (ℝ)

Distinct473
Distinct (%)12.9%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2.5340874
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-03-25T15:44:57.885878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.525
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9804233
Coefficient of variation (CV)1.1761328
Kurtosis14.936249
Mean2.5340874
Median Absolute Deviation (MAD)0.725
Skewness3.2795099
Sum9274.76
Variance8.8829228
MonotonicityNot monotonic
2024-03-25T15:44:57.980994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.6%
0.95 52
 
1.4%
2 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

Distinct2650
Distinct (%)72.4%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean13894.498
Minimum5
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-03-25T15:44:58.070299image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile4716.95
Q16817.25
median9020
Q313880.5
95-th percentile33333
Maximum600000
Range599995
Interquartile range (IQR)7063.25

Descriptive statistics

Standard deviation23209.236
Coefficient of variation (CV)1.6703903
Kurtosis186.95158
Mean13894.498
Median Absolute Deviation (MAD)2791.5
Skewness11.438233
Sum50853864
Variance5.3866863 × 108
MonotonicityNot monotonic
2024-03-25T15:44:58.162103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
22222 13
 
0.4%
11111 13
 
0.4%
6666 13
 
0.4%
7500 12
 
0.3%
8333 12
 
0.3%
6000 11
 
0.3%
Other values (2640) 3509
95.5%
(Missing) 16
 
0.4%
ValueCountFrequency (%)
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
151 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

SKEWED 

Distinct2676
Distinct (%)73.1%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2650.0874
Minimum50
Maximum642857.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-03-25T15:44:58.256363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile518.88476
Q11231.97
median1733.0125
Q32300.0984
95-th percentile4235.5342
Maximum642857.14
Range642807.14
Interquartile range (IQR)1068.1284

Descriptive statistics

Standard deviation18133.153
Coefficient of variation (CV)6.8424734
Kurtosis1052.0565
Mean2650.0874
Median Absolute Deviation (MAD)532.9623
Skewness31.623594
Sum9699320
Variance3.2881123 × 108
MonotonicityNot monotonic
2024-03-25T15:44:58.352388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3240 43
 
1.2%
2700 36
 
1.0%
2000 33
 
0.9%
1800 32
 
0.9%
900 28
 
0.8%
2250 20
 
0.5%
1350 20
 
0.5%
1000 18
 
0.5%
1650.165017 17
 
0.5%
4500 17
 
0.5%
Other values (2666) 3396
92.4%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72.00115202 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
642857.1429 1
< 0.1%
620000 1
< 0.1%
566666.6667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517.24138 2
0.1%
65261 1
< 0.1%
58227.8481 1
< 0.1%
55000 1
< 0.1%
Distinct2353
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size428.1 KiB
2024-03-25T15:44:58.758151image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.241023
Min length12

Characters and Unicode

Total characters199390
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1846 ?
Unique (%)50.2%

Sample

1st rowSuper Built up area 1671(155.24 sq.m.)Built Up area: 1190 sq.ft. (110.55 sq.m.)Carpet area: 970 sq.ft. (90.12 sq.m.)
2nd rowSuper Built up area 2400(222.97 sq.m.)Built Up area: 2200 sq.ft. (204.39 sq.m.)Carpet area: 2000 sq.ft. (185.81 sq.m.)
3rd rowSuper Built up area 2176(202.16 sq.m.)
4th rowBuilt Up area: 148 (123.75 sq.m.)
5th rowCarpet area: 1850 (171.87 sq.m.)
ValueCountFrequency (%)
area 5572
18.5%
sq.m 3654
12.1%
up 3020
 
10.0%
built 2316
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 682
 
2.3%
plot 681
 
2.3%
Other values (2842) 8698
28.9%
2024-03-25T15:44:59.243045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26460
 
13.3%
. 20385
 
10.2%
a 13151
 
6.6%
r 9454
 
4.7%
e 9318
 
4.7%
1 9202
 
4.6%
s 7566
 
3.8%
q 7430
 
3.7%
t 7323
 
3.7%
u 6770
 
3.4%
Other values (25) 82331
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26460
 
13.3%
. 20385
 
10.2%
a 13151
 
6.6%
r 9454
 
4.7%
e 9318
 
4.7%
1 9202
 
4.6%
s 7566
 
3.8%
q 7430
 
3.7%
t 7323
 
3.7%
u 6770
 
3.4%
Other values (25) 82331
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26460
 
13.3%
. 20385
 
10.2%
a 13151
 
6.6%
r 9454
 
4.7%
e 9318
 
4.7%
1 9202
 
4.6%
s 7566
 
3.8%
q 7430
 
3.7%
t 7323
 
3.7%
u 6770
 
3.4%
Other values (25) 82331
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26460
 
13.3%
. 20385
 
10.2%
a 13151
 
6.6%
r 9454
 
4.7%
e 9318
 
4.7%
1 9202
 
4.6%
s 7566
 
3.8%
q 7430
 
3.7%
t 7323
 
3.7%
u 6770
 
3.4%
Other values (25) 82331
41.3%

bedRoom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3609902
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-03-25T15:44:59.342732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.897651
Coefficient of variation (CV)0.56461068
Kurtosis18.210805
Mean3.3609902
Median Absolute Deviation (MAD)1
Skewness3.4847341
Sum12355
Variance3.6010792
MonotonicityNot monotonic
2024-03-25T15:44:59.416368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 940
25.6%
4 661
18.0%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 940
25.6%
3 1496
40.7%
4 661
18.0%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4254625
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-03-25T15:44:59.490015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9480726
Coefficient of variation (CV)0.56870353
Kurtosis17.54133
Mean3.4254625
Median Absolute Deviation (MAD)1
Skewness3.2484763
Sum12592
Variance3.7949869
MonotonicityNot monotonic
2024-03-25T15:44:59.572553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1045
28.4%
4 821
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1045
28.4%
3 1077
29.3%
4 821
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.1 KiB
3+
1173 
3
1073 
2
883 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3190968
Min length1

Characters and Unicode

Total characters4849
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3+
3rd row3
4th row0
5th row2

Common Values

ValueCountFrequency (%)
3+ 1173
31.9%
3 1073
29.2%
2 883
24.0%
1 365
 
9.9%
0 182
 
5.0%

Length

2024-03-25T15:44:59.656514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T15:44:59.726497image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 2246
61.1%
2 883
 
24.0%
1 365
 
9.9%
0 182
 
5.0%

Most occurring characters

ValueCountFrequency (%)
3 2246
46.3%
+ 1173
24.2%
2 883
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2246
46.3%
+ 1173
24.2%
2 883
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2246
46.3%
+ 1173
24.2%
2 883
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2246
46.3%
+ 1173
24.2%
2 883
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7954608
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-03-25T15:44:59.807942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0133651
Coefficient of variation (CV)0.8849091
Kurtosis4.5160887
Mean6.7954608
Median Absolute Deviation (MAD)3
Skewness1.6945505
Sum24851
Variance36.160559
MonotonicityNot monotonic
2024-03-25T15:44:59.902433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 494
13.4%
1 351
 
9.5%
4 316
 
8.6%
8 194
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 936
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 351
9.5%
2 494
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 194
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size253.0 KiB
NA
1044 
North-East
624 
East
622 
North
387 
West
249 
Other values (4)
750 

Length

Max length10
Median length5
Mean length5.4657236
Min length2

Characters and Unicode

Total characters20092
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast
2nd rowNorth-East
3rd rowNA
4th rowNA
5th rowEast

Common Values

ValueCountFrequency (%)
NA 1044
28.4%
North-East 624
17.0%
East 622
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.3%
South-East 173
 
4.7%
South-West 153
 
4.2%

Length

2024-03-25T15:44:59.994162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T15:45:00.085268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
na 1044
28.4%
north-east 624
17.0%
east 622
16.9%
north 387
 
10.5%
west 249
 
6.8%
south 231
 
6.3%
north-west 193
 
5.3%
south-east 173
 
4.7%
south-west 153
 
4.2%

Most occurring characters

ValueCountFrequency (%)
t 3775
18.8%
N 2248
11.2%
s 2014
10.0%
o 1761
8.8%
h 1761
8.8%
E 1419
 
7.1%
a 1419
 
7.1%
r 1204
 
6.0%
- 1143
 
5.7%
A 1044
 
5.2%
Other values (4) 2304
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3775
18.8%
N 2248
11.2%
s 2014
10.0%
o 1761
8.8%
h 1761
8.8%
E 1419
 
7.1%
a 1419
 
7.1%
r 1204
 
6.0%
- 1143
 
5.7%
A 1044
 
5.2%
Other values (4) 2304
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3775
18.8%
N 2248
11.2%
s 2014
10.0%
o 1761
8.8%
h 1761
8.8%
E 1419
 
7.1%
a 1419
 
7.1%
r 1204
 
6.0%
- 1143
 
5.7%
A 1044
 
5.2%
Other values (4) 2304
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3775
18.8%
N 2248
11.2%
s 2014
10.0%
o 1761
8.8%
h 1761
8.8%
E 1419
 
7.1%
a 1419
 
7.1%
r 1204
 
6.0%
- 1143
 
5.7%
A 1044
 
5.2%
Other values (4) 2304
11.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.4 KiB
Relatively New
1645 
New Property
593 
Moderately Old
563 
Undefined
307 
Old Property
303 

Length

Max length18
Median length14
Mean length13.383297
Min length9

Characters and Unicode

Total characters49197
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowRelatively New
3rd rowModerately Old
4th rowUndefined
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New 1645
44.7%
New Property 593
 
16.1%
Moderately Old 563
 
15.3%
Undefined 307
 
8.4%
Old Property 303
 
8.2%
Under Construction 265
 
7.2%

Length

2024-03-25T15:45:00.176831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T15:45:00.248465image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
new 2238
31.8%
relatively 1645
23.3%
property 896
12.7%
old 866
 
12.3%
moderately 563
 
8.0%
undefined 307
 
4.4%
under 265
 
3.8%
construction 265
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8429
17.1%
l 4719
 
9.6%
t 3634
 
7.4%
3369
 
6.8%
y 3104
 
6.3%
r 2885
 
5.9%
d 2308
 
4.7%
N 2238
 
4.5%
w 2238
 
4.5%
i 2217
 
4.5%
Other values (15) 14056
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8429
17.1%
l 4719
 
9.6%
t 3634
 
7.4%
3369
 
6.8%
y 3104
 
6.3%
r 2885
 
5.9%
d 2308
 
4.7%
N 2238
 
4.5%
w 2238
 
4.5%
i 2217
 
4.5%
Other values (15) 14056
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8429
17.1%
l 4719
 
9.6%
t 3634
 
7.4%
3369
 
6.8%
y 3104
 
6.3%
r 2885
 
5.9%
d 2308
 
4.7%
N 2238
 
4.5%
w 2238
 
4.5%
i 2217
 
4.5%
Other values (15) 14056
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8429
17.1%
l 4719
 
9.6%
t 3634
 
7.4%
3369
 
6.8%
y 3104
 
6.3%
r 2885
 
5.9%
d 2308
 
4.7%
N 2238
 
4.5%
w 2238
 
4.5%
i 2217
 
4.5%
Other values (15) 14056
28.6%

super_built_up_area
Real number (ℝ)

MISSING 

Distinct593
Distinct (%)31.6%
Missing1801
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-03-25T15:45:00.343914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.5
Variance583959.12
MonotonicityNot monotonic
2024-03-25T15:45:00.440150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 37
 
1.0%
1650 37
 
1.0%
1578 25
 
0.7%
2000 25
 
0.7%
2150 22
 
0.6%
1640 22
 
0.6%
2408 19
 
0.5%
1900 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (583) 1634
44.5%
(Missing) 1801
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

MISSING 

Distinct643
Distinct (%)38.0%
Missing1986
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean1945.0817
Minimum2
Maximum13500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-03-25T15:45:00.534208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4680
Maximum13500
Range13498
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation1453.1144
Coefficient of variation (CV)0.74707114
Kurtosis7.4161776
Mean1945.0817
Median Absolute Deviation (MAD)650
Skewness2.1133193
Sum3287188
Variance2111541.4
MonotonicityNot monotonic
2024-03-25T15:45:00.632909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
2700 33
 
0.9%
1350 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
2000 24
 
0.7%
1300 24
 
0.7%
1700 23
 
0.6%
Other values (633) 1387
37.7%
(Missing) 1986
54.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%
7450 1
 
< 0.1%

carpet_area
Real number (ℝ)

MISSING  SKEWED 

Distinct732
Distinct (%)39.1%
Missing1805
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2530.2228
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-03-25T15:45:00.730826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1845
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)945

Descriptive statistics

Standard deviation22805.887
Coefficient of variation (CV)9.0133909
Kurtosis604.21426
Mean2530.2228
Median Absolute Deviation (MAD)470
Skewness24.326755
Sum4734046.9
Variance5.2010849 × 108
MonotonicityNot monotonic
2024-03-25T15:45:00.836317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1600 35
 
1.0%
1800 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1000 22
 
0.6%
1450 22
 
0.6%
Other values (722) 1577
42.9%
(Missing) 1805
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size244.1 KiB
0.0
2971 
1.0
705 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11028
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2971
80.8%
1.0 705
 
19.2%

Length

2024-03-25T15:45:00.939906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T15:45:01.015687image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2971
80.8%
1.0 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 6647
60.3%
. 3676
33.3%
1 705
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6647
60.3%
. 3676
33.3%
1 705
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6647
60.3%
. 3676
33.3%
1 705
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6647
60.3%
. 3676
33.3%
1 705
 
6.4%

servant room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size244.1 KiB
0.0
2348 
1.0
1328 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11028
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2348
63.9%
1.0 1328
36.1%

Length

2024-03-25T15:45:01.093131image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T15:45:01.174872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2348
63.9%
1.0 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 6024
54.6%
. 3676
33.3%
1 1328
 
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6024
54.6%
. 3676
33.3%
1 1328
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6024
54.6%
. 3676
33.3%
1 1328
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6024
54.6%
. 3676
33.3%
1 1328
 
12.0%

store room
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size244.1 KiB
0.0
3338 
1.0
338 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11028
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3338
90.8%
1.0 338
 
9.2%

Length

2024-03-25T15:45:01.267550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T15:45:01.341551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3338
90.8%
1.0 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 7014
63.6%
. 3676
33.3%
1 338
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7014
63.6%
. 3676
33.3%
1 338
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7014
63.6%
. 3676
33.3%
1 338
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7014
63.6%
. 3676
33.3%
1 338
 
3.1%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size244.1 KiB
0.0
3020 
1.0
656 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11028
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3020
82.2%
1.0 656
 
17.8%

Length

2024-03-25T15:45:01.419631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T15:45:01.490101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3020
82.2%
1.0 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 6696
60.7%
. 3676
33.3%
1 656
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6696
60.7%
. 3676
33.3%
1 656
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6696
60.7%
. 3676
33.3%
1 656
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6696
60.7%
. 3676
33.3%
1 656
 
5.9%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size244.1 KiB
0.0
3271 
1.0
405 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11028
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3271
89.0%
1.0 405
 
11.0%

Length

2024-03-25T15:45:01.570378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T15:45:01.640893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3271
89.0%
1.0 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 6947
63.0%
. 3676
33.3%
1 405
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6947
63.0%
. 3676
33.3%
1 405
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6947
63.0%
. 3676
33.3%
1 405
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11028
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6947
63.0%
. 3676
33.3%
1 405
 
3.7%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
1
2403 
0
1062 
2
 
211

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 2403
65.4%
0 1062
28.9%
2 211
 
5.7%

Length

2024-03-25T15:45:01.718308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T15:45:01.789399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2403
65.4%
0 1062
28.9%
2 211
 
5.7%

Most occurring characters

ValueCountFrequency (%)
1 2403
65.4%
0 1062
28.9%
2 211
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2403
65.4%
0 1062
28.9%
2 211
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2403
65.4%
0 1062
28.9%
2 211
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2403
65.4%
0 1062
28.9%
2 211
 
5.7%

luxury_score
Real number (ℝ)

ZEROS 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.505985
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-03-25T15:45:01.873894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.062732
Coefficient of variation (CV)0.742074
Kurtosis-0.88002211
Mean71.505985
Median Absolute Deviation (MAD)38
Skewness0.45942848
Sum262856
Variance2815.6536
MonotonicityNot monotonic
2024-03-25T15:45:01.975595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 59
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2313
62.9%
ValueCountFrequency (%)
0 462
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2024-03-25T15:44:54.568599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:48.549406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.190492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.776215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.474270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.166382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.812104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.548480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.187685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.862663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:54.634815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:48.667545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.240712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.838265image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.555025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.229720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.873124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.612174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.259659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.926074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:54.701256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:48.781742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.300442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.914902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.622131image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.298434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.932678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.680736image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.327030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.997631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:55.235464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:48.849786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.360113image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.006658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.690872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.361577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.993476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.741529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.392940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:54.063459image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:55.294594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:48.900237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.420098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.084946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.760517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.428980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.060844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.809474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.471267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:54.132809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:55.353155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:48.951030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.481517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.153260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.833450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.497650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.175494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.881181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.544154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:54.206927image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:55.409876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:48.997620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.536291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.213133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.905826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.558745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.261377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.939821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.609994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:54.270873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:55.470136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.043439image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.591653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.270907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.970297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.620982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.325721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.000481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.666628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:54.344534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:55.544544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.092991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.652549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.342035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.036155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.687349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.409845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.053485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.734315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:54.429853image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:55.609039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.140257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:49.713775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:50.410019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.098014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:51.747619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:52.481405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.119764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:53.793759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T15:44:54.497790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-25T15:44:55.724980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-25T15:44:55.914433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-25T15:44:56.056119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatss the leafsector 851.2012371.0970.010508Super Built up area 1671(155.24 sq.m.)Built Up area: 1190 sq.ft. (110.55 sq.m.)Carpet area: 970 sq.ft. (90.12 sq.m.)2.02.024.0EastRelatively New1671.01190.0970.0000000.01.01.00.00.0181
1flattulip purplesector 691.809000.02000.000000Super Built up area 2400(222.97 sq.m.)Built Up area: 2200 sq.ft. (204.39 sq.m.)Carpet area: 2000 sq.ft. (185.81 sq.m.)4.05.03+4.0North-EastRelatively New2400.02200.02000.0000000.01.00.00.00.01165
2flatumang monsoon breezesector 781.155284.02176.381529Super Built up area 2176(202.16 sq.m.)3.03.039.0NAModerately Old2176.0NaNNaN0.01.00.00.01.0122
3houseindependentsector 72.2516892.01332.000000Built Up area: 148 (123.75 sq.m.)5.03.002.0NAUndefinedNaN148.0NaN0.00.00.00.00.010
4flattulip violetsector 691.759459.01850.089862Carpet area: 1850 (171.87 sq.m.)3.04.020.0EastRelatively NewNaNNaN1850.0000000.00.00.00.00.00174
5flatshri ram apartmentssector 40.465111.0900.019566Carpet area: 900 (83.61 sq.m.)2.02.011.0EastRelatively NewNaNNaN900.0000000.00.00.00.00.0069
6flatbestech park view residencysector 21.027208.01415.094340Super Built up area 1415(131.46 sq.m.)2.02.0311.0South-WestModerately Old1415.0NaNNaN0.00.00.00.00.0192
7flatpyramid elitesector 860.457739.0581.470474Carpet area: 581.41 (54.01 sq.m.)2.02.014.0NAUnder ConstructionNaNNaN581.3582390.00.00.00.00.0166
8flatlaxmi apartment sector 99a gurgaonsector 99a0.304615.0650.054171Super Built up area 650(60.39 sq.m.)Carpet area: 550 sq.ft. (51.1 sq.m.)2.02.014.0NANew Property650.0NaN550.0000000.00.00.00.00.0144
9flatvatika citysector 491.727818.02200.051164Carpet area: 2200 (204.39 sq.m.)3.03.022.0North-EastOld PropertyNaNNaN2200.0000000.00.00.00.00.00144
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3792flatemaar digihomessector 622.1014254.01473.270661Super Built up area 1508.26(140.12 sq.m.)2.02.0230.0NorthNew Property1508.26NaNNaN0.01.00.00.00.0152
3793housesobha citysector 10810.5014323.07331.000000Built Up area: 7331 (681.07 sq.m.)5.04.03+1.0EastUndefinedNaN7331.0NaN0.00.00.00.00.0159
3794flatgreenopolissector 890.804820.01659.751037Super Built up area 1660(154.22 sq.m.)2.03.038.0NAUnder Construction1660.00NaNNaN1.00.00.00.01.0160
3795houseindependentsector 1050.3876000.050.000000Carpet area: 50 (4.65 sq.m.)2.02.021.0NAUndefinedNaNNaN50.0000000.00.00.00.00.010
3796flatrof anandasector 950.205463.0366.099213Carpet area: 366.08 (34.01 sq.m.)1.01.019.0South-WestRelatively NewNaNNaN366.0802390.00.00.01.00.0152
3797flatgodrej iconsector 88a1.6210018.01617.089239Super Built up area 1617(150.22 sq.m.)3.02.03+10.0EastRelatively New1617.00NaNNaN1.00.00.00.00.0249
3798flatm3m skywalksector 741.5012500.01200.000000Super Built up area 1400(130.06 sq.m.)Built Up area: 1300 sq.ft. (120.77 sq.m.)Carpet area: 1200 sq.ft. (111.48 sq.m.)2.02.0317.0North-EastRelatively New1400.001300.01200.0000000.00.01.00.00.01174
3799houseansal sushant lok 2sector 563.4022222.01530.000000Plot area 170(142.14 sq.m.)6.06.03+2.0WestModerately OldNaN1530.0NaN0.00.00.00.00.0029
3800flattulip violetsector 691.708445.02013.025459Super Built up area 2010(186.74 sq.m.)4.04.027.0WestRelatively New2010.00NaNNaN0.00.00.00.00.01128
3801flatgodrej nature plussector 331.2014379.0834.550386Carpet area: 77.532.02.031.0NANew PropertyNaNNaN77.5300000.00.00.00.00.0165